Theor Appl Climatol (2014) 115:713–729
DOI 10.1007/s00704-013-0917-x
ORIGINAL PAPER
Assessing the performance of satellite-based precipitation
products and its dependence on topography over Poyang
Lake basin
Xianghu Li & Qi Zhang & Chong-Yu Xu
Received: 3 September 2012 / Accepted: 22 April 2013 / Published online: 26 May 2013
# Springer-Verlag Wien 2013
Abstract Satellite-based precipitation products (SPPs) have
greatly improved their applicability and are expected to
offer an alternative to ground-based precipitation estimates
in the present and the foreseeable future. There is a strong
need for a quantitative evaluation of the usefulness and
limitations of SPPs in operational meteorology and hydrology. This study compared two widely used high-resolution
SPPs, the Tropical Rainfall Measuring Mission (TRMM)
and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) in Poyang Lake basin which is located in the middle reach of the
Yangtze River in China. The bias of rainfall amount and
occurrence frequency under different rainfall intensities and
the dependence of SPPs performance on elevation and slope
were investigated using different statistical indices. The results revealed that (1) TRMM 3B42 usually underestimates
the rainy days and overestimates the average rainfall as well
as annual rainfall, while the PERSIANN data were markedly lower than rain gauge data; (2) the rainfall contribution
rates were underestimated by TRMM 3B42 in the middle
rainfall class but overestimated in the heavy rainfall class,
X. Li : Q. Zhang (*)
State Key Laboratory of Lake Science and Environment,
Nanjing Institute of Geography and Limnology,
Chinese Academy of Sciences, 73 East Beijing Road,
Nanjing 210008, People’s Republic of China
e-mail: qzhang@niglas.ac.cn
X. Li
e-mail: xhli@niglas.ac.cn
C.<Y. Xu
Department of Hydrology and Water Resources,
Wuhan University, Wuhan, China
e-mail: c.y.xu@geo.uio.no
C.<Y. Xu
Department of Geosciences, University of Oslo, Oslo, Norway
while the opposite trend was observed for PERSIANN; (3)
although the temporal distribution characteristics of monthly
rainfall were correctly described by both SPPs, PERSIANN
tended to suffer a systematic underestimation of rainfall in
every month; and (4) the performances of both SPPs had
clear dependence on elevation and slope, and their relationships can be fitted using quadratic equations.
1 Introduction
Precipitation is the key input for hydrological modeling
and its temporal and spatial distribution has a significant
impact on the land surface hydrological fluxes and
states (Gottschalck et al. 2005; Tian et al. 2007; Su et
al. 2008). Therefore, accurate measurements of precipitation on fine spatial and temporal scales are very
important for simulating land surface hydrologic processes, predicting drought and flood, and monitoring
water resources (Sorooshian et al. 2005; Yong et al.
2010). However, in many populated regions of the
world and especially in developing countries, groundbased measurement networks (either from rain gauge or
weather radar) are either sparse in both time and space
or nonexistent (Behrangi et al. 2011), and their limited
sampling areas and problems inherent in point measurements represent a substantial difficulty when dealing
with effective spatial coverage of rainfall over a large
area (Pegram et al. 2004; Schulze 2006; Ghile et al.
2010). Although weather radar has enormous potential
to offer rainfall estimates with high spatial resolution
and temporal continuity (Sun et al. 2000; He et al.
2011), there is often a large space–time variable bias
(Smith et al. 2007; Krajewski and Smith 2002) and its
accuracy is highly sensitive to atmospheric conditions,
sampling height of the radar beam, beam blocking,
714
variations in the reflectivity–rainfall rate relationships,
ground echoes, and distance from the radar (Deyzel et
al. 2004; Pegram et al. 2004; Piccolo and Chirico
2005). This situation restricts these regions to manage
water resources (Behrangi et al. 2011) and hampers the
development and use of flood and drought warning
models, extreme weather monitoring, and decisionmaking systems (AghaKouchak et al. 2011).
Alternatively, satellite-based precipitation products (SPPs)
are widely accepted as promising strategies to address the
previously mentioned limitations (Ghile et al. 2010). Such
data are especially valuable in developing countries or remote
locations, where conventional rain gauge or weather radar
data are sparse or of bad quality (Hughes 2006). Furthermore,
the near real-time availability of the SPPs makes them suitable
for modeling applications where water resources management
is crucial and data gathering and quality assurance are cumbersome (Stisen and Sandholt 2010). Recent development in
global and regional SPPs has greatly improved their applicability as input to large-scale distributed hydrological models
(Stisen and Sandholt 2010; Li et al. 2012, 2013; Samaniego et
al. 2012) and are expected to offer an alternative to groundbased rainfall estimates in the present and the foreseeable
future (Sawunyama and Hughes 2008). This is mainly due
to the increased temporal and spatial resolution of SPPs and
also due to improved accuracy resulting from new methods to
merge various data sources such as radar, microwave, and
thermal infrared (TIR) remote sensing (Gottschalck et al.
2005; Tian and Peters-Lidard 2007; Stisen and Sandholt
2010).
With suites of sensors flying on a variety of satellites over
the last two decades, many satellite-based precipitation estimation algorithms have been developed (Behrangi et al. 2011)
to combine measurements of different spaceborne sensors and
gauge data allow the derivation of high-quality precipitation
estimates. Since Huffman et al. (1995, 1997) created a scheme
to combine satellite data of different sensors (microwave,
infrared [IR], and longwave radiation) with gauge data and
built the Global Precipitation Climatology Project (GPCP)
combined precipitation dataset at a 2.5×2.5° grid and monthly
resolution, these algorithms have improved constantly by
emerging further multisource products with higher resolutions
(Scheel et al. 2011). Currently, several satellite-based gridded
precipitation estimates are available for (at least) the lower
latitudes and the tropics at high temporal (three hourly or
shorter) and reasonably high spatial (0.25×0.25° or finer)
resolutions. Examples include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis
(TMPA) (Huffman et al. 2007), the Climate Prediction Center
(CPC) morphing algorithm (CMORPH) (Joyce et al. 2004),
the Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) (Hsu et al.
1997; Sorooshian et al. 2000), the Global Satellite Mapping of
X. Li et al.
Precipitation (Kubota et al. 2007; Aonashi et al. 2009; Ushio
et al. 2009), the Naval Research Laboratory Global BlendedStatistical Precipitation Analysis (Turk et al. 2000), and so on.
Although different in the precipitation estimation procedure,
in all of the listed products, combined information from passive microwave (PMW) sensors in low earth orbiting satellites
and IR radiometers in geostationary earth orbiting (GEO)
satellites is used to improve the consistency, accuracy, coverage, and timeliness of high-resolution precipitation data
(Kubota et al. 2009; Behrangi et al. 2011).
However, satellite data also suffer from some inherent
shortcomings and have biases and random errors that are
caused by various factors like sampling frequency,
nonuniform field of view of the sensors, and uncertainties
in the rainfall retrieval algorithms (Nair et al. 2009). It is,
therefore, essential to validate the satellite-derived products
with conventional rain estimates to quantify the direct usability of these products (Nair et al. 2009; Li et al. 2012,
2013). Numerous researchers have examined the quality of
satellite-derived precipitation datasets in various regions of
the world. Table 1 summarizes recent studies on evaluations
of three widely used high-resolution SPPs (TMPA,
CMORPH, and PERSIANN) based on the work of Romilly
and Gebremichael (2011). For instance, Dinku et al. (2010)
evaluated two satellite rainfall estimation algorithms, TMPA
and CMORPH, over two locations (highlands of Ethiopia
and Columbia) and found that total rainfall amount was
overestimated by TMPA 3B42RT (13 %) and CMORPH
(11 %) in Ethiopia, while it was underestimated by TMPA
3B42RT (17 %), TMPA 3B42 (16 %), and CMORPH (9 %)
in Columbia. Stisen and Sandholt (2010) evaluated five
satellite products, including TRMM 3B42, CMORPH, and
PERSIANN, in the Senegal River Basin using the MIKE
SHE hydrological model and found that TRMM 3B42 performs better than the other satellite products. Yamamoto et
al. (2011) compared several SPPs with rainfall data from the
automated weather station in the Nepal Himalayas and
found that PERSIANN showed large differences with the
observed values in winter and CMORPH had a tendency to
overestimate precipitation in the pre-monsoon and postmonsoon seasons. Ward et al. (2011) believed that TRMM
3B42 and PERSIANN are unable to detect light rainfall
amounts and underestimate rainfall in the dry season.
Behrangi et al. (2011) found that TMPA 3B42RT,
CMORPH, and PERSIANN tend to overestimate intense
precipitation during warm months. In addition, several studies have also validated the effects of topography on SPPs
performance. For example, Bitew and Gebremichael (2010)
found that the CMORPH and PERSIANN-Cloud Classification System (CCS) underestimated 32 and 49 % of total
rainfall, respectively, in a high-elevation region. Hong et al.
(2007) evaluated the impact of topography on the performance of PERSIANN-CCS in western Mexico and found
Assessing the performance of satellite-based precipitation products
715
Table 1 Evaluations on high-resolution SPPs (supplement based on Romilly and Gebremichael 2011)
Precipitation Regions
products
Main results
References
TMPA 3B42
TMPA
3B42RT
CMORPH
PERSIANN
TMPA 3B42
CMORPH
PERSIANN
TRMM
3B42
CMORPH
PERSIANN
TRMM
3B42
PERSIANN
TMPA
3B42RT
CMORPH
PERSIANN
TRMM
3B42
CMORPH
PERSIANN
TMPA 3B42
TMPA
3B42RT
CMORPH
TMPA 3B42
TMPA
3B42RT
CMORPH
TMPA
3B42RT
CMORPH
PERSIANN
TMPA 3B42
CMORPH
PERSIANN
CMORPH
PERSIANNCCS
Illinois River basin, USA
TMPA 3B42RT, CMORPH, and PERSIANN tend to overestimate intense
precipitation during warm months; CMORPH demonstrates higher skill
to delineate precipitation area
Behrangi et al.
(2011)
Nepal Himalayas
PERSIANN showed large differences in winter; CMORPH overestimates rainfall Yamamoto et al.
in the pre-monsoon and post-monsoon seasons; TMPA 3B42
(2011)
and CMORPH increase rainfall during the morning
Congo River basin
TRMM 3B42 provides the best spatial and temporal distributions and magnitudes; Beighley et al.
CMORPH and PERSIANN tend to overestimate magnitudes
(2011)
Paute River, Ecuador and
Baker basin, Patagonia
Ward et al. (2011)
Awash River basin,
Ethiopia
Both are unable to detect light rainfall amounts and underestimated in the
dry season; there is a systematic underestimation of rainfall occurrence
by TRMM 3B42
3B42RT and CMORPH show an increasing trend with elevation;
PERSIANN considerably underestimates rainfall in high-elevation areas
Senegal River Basin, West
African
CMORPH and PERSIANN have much larger biases than TRMM based
on MIKE SHE hydrological model
Stisen and
Sandholt (2010)
Western Highlands,
Ethiopia
Occurrence of rain underestimated by all products; total amount underestimated by Dinku et al.
TMPA 3B42 (14 %) and overestimated by TMPA 3B42RT (13 %) and
(2010)
CMORPH (11 %)
Highlands, Columbia
Occurrence of rain underestimated by all products; Total amount underestimated Dinku et al.
by TMPA 3B42RT (17 %), TMPA 3B42 (16 %), and CMORPH (9 %)
(2010)
Great Rift Valley, Ethiopia
TMPA 3B42RT and CMORPH show elevation-dependent trends,
with underestimation at higher elevations; PERSIANN underestimates
at higher elevations and not exhibit elevation-dependent trends
Hirpa et al. (2010)
Hirpa et al. (2010)
USA and the Pacific Ocean CMORPH and PERSIANN overestimate rainfall as much as 125 %
in warm season over the USA; CMORPH and TMPA 3B42
underestimate rainfall over the Pacific Ocean
Sapiano and
Arkin (2009)
Berressa basin, Ethiopia
Both underestimate heavy rainfall by 50 %; total amount underestimated
by CMORPH (32 %) and PERSIANN-CCS (49 %)
Bitew and
Gebremichael
(2010)
Overestimate rainfall <1 mm/day; underestimate rainfall >1 mm/day
Yu et al. (2009)
TMPA 3B42 Mainland China
CMORPH
TRMM
Sierra Madre Occidental,
3B42
Mexico
CMORPH
PERSIANN
TRMM
Ethiopia and Zimbabwe
3B42
TRMM
3B42RT
CMORPH
PERSIANN
CMORPH and PERSIANN overestimate the rainfall rate and frequency; TRMM Nesbitt et al.
3B42 closely agrees with the rain gauge network
(2008)
All data detected the occurrence of rainfall well, the amount
of rainfall was poorly estimated; the performance was better over
Zimbabwe (relatively flat area) as compared with Ethiopia (complex terrain)
Dinku et al.
(2008)
716
X. Li et al.
Table 1 (continued)
Precipitation Regions
products
Main results
TMPA 3B42 Continental USA
CMORPH
TMPA 3B42 has near zero biases for both summer and winter months; CMORPH Tian et al. (2007)
overestimates rainfall over central; underestimates over the northeast during the
summer
PERSIANN overestimate total rainfall over central and western during
Gottschalck et al.
spring and summer, underestimate during fall and winter; TMPA 3B42RT
(2005)
overestimate during spring and summer, overestimate during fall and winter
TMPA
3B42RT
PERSIANN
Continental USA
that light precipitation events were underestimated in the
high-elevation regions and precipitation events in the lowerelevation regions were overestimated. Hirpa et al. (2010)
also found elevation-dependent trends of performance in the
TMPA 3B42RT and CMORPH products.
Many researchers have testified that the accuracy of SPPs
is influenced by location, season, rain type (i.e., convective,
stratiform), topography, climatological factors, and so on
(Artan et al. 2007; Dinku et al. 2008; Jiang et al. 2008;
Han et al. 2011). However, very few of previous studies
have so far fully and comprehensively analyzed these aspects. Their performances under different rainfall intensities
and in different seasons are still unclear. Moreover, although
several recent studies (i.e., Dinku et al. 2008; Ward et al.
2011; Romilly and Gebremichael 2011) have taken into
account the effects of elevation, they have only compared
the performances of SPPs in high-elevation and lowelevation regions and the quantitative relationships between
accuracy and elevation are not mentioned. On the other
hand, slope as another important topographic factor is not
considered yet in these corresponding studies; the relationship between SPPs performance and slope is still unclear.
Poyang Lake, located in the middle reach of the Yangtze
River (Fig. 1), is the largest freshwater lake in China and
plays a crucial role in flood protection for the lower reaches
of the Yangtze River. It has recently been shown that the
frequency and severity of the floods have increased since
1990 (Guo et al. 2008) and the surface runoffs from the five
subbasins have been the primary source of the major floods
in the Poyang Lake basin (Hu et al. 2007). To implement
flood protection and regulation and ensure water safety in
areas around the lake, it is necessary to understand the flood
development and the rainfall–runoff processes in the catchment. However, the applications of satellite-based precipitation, as complementary rainfall data, are seldom in Poyang
Lake basin. The scarcity on the accuracy evaluation of SPPs
in this basin has hampered their extensive application and
development of flood warning models to a certain extent.
Therefore, the objectives of the study are designed to evaluate and compare two high-resolution SPPs (TRMM 3B42
and PERSIANN) with rain gauge data and investigate their
spatial and temporal characteristics in the Poyang Lake
References
basin. Also, the bias of rainfall amount and occurrence
frequency under different rainfall intensities in each month
and the quantitative relationships of accuracy with elevation
and slope are investigated. By doing so, different statistical
measures and methods are calculated and used in the study.
The study is expected to serve as useful reference and
valuable information for future study and application of
satellite rainfall data in the Poyang Lake basin as well as
in other regions.
The rest of this paper is organized as follows. In the next
section, details of the study area and climate, along with a
brief discussion on the rain gauge and SPPs, are presented.
In Section 3, the indexes and methods used in the study are
briefly described with the help of cited references. Major
results of this study are presented in Section 4. Section 5
mainly discusses the possible sources of errors of SPPs from
various aspects and the further challenges that we face in
using SPPs for hydrological studies, and Section 6 summarizes the conclusions.
2 Study area and data
2.1 Study area
Poyang Lake basin is located in the middle and lower
reaches of the Yangtze River, China and the lake receives
water flows mainly from the five rivers: Xiushui River,
Ganjiang River, Fuhe River, Xinjiang River, and Raohe
River and discharges into the Yangtze River through a
channel in its northern part (Fig. 1). The total drainage area
of the water systems is 16.22×104 km2, accounting for 9 %
of the drainage area of the Yangtze River basin. The topography in the basin varies from highly mountainous and hilly
areas (with the maximum elevation of 2,200 m above mean
sea level) to alluvial plains in the lower reaches of the
primary watercourses. Poyang Lake basin has a subtropical
wet climate characterized with a mean annual precipitation
of 1,680 mm for the period of 1960–2007 and annual mean
temperature of 17.5 °C. Annual precipitation shows a wet
season and a dry season and a short transition period in
between. Precipitation increases quickly from January to
Assessing the performance of satellite-based precipitation products
June and decreases sharply in July, and after September, the
dry season sets in and lasts through December. In response
to the annual cycle of precipitation, the Poyang Lake can
expand to a large water surface of 3,800 km2 and volume of
320×108 m3 in the wet season, but shrinks to little more
than a river during the dry season (Xu et al. 2001) and
exposes extensive floodplains and wetland areas.
2.2 Data
717
averaged to obtain the areal daily precipitation for the
Poyang Lake basin, and the spatial distribution of annual rainfall is interpolated by the inverse distance
weighted (IDW) technique with a power of 2. In addition, the digital elevation model data are derived from
the National Aeronautics and Space Administration
(NASA) Shuttle Radar Topographic Mission at a spatial
resolution of 90 m (http://srtm.csi.cgiar.org), which are
used to obtain the altitude of each rain gauge and the
average slope in pixel size of 0.25×0.25°.
2.2.1 Ground data
2.2.2 Satellite data
Daily precipitation data, during the period 2000–2007
for 34 stations in the Poyang Lake basin, are obtained
from National Meteorological Information Center of
China, which are used to compare and evaluate the
accuracy of satellite-based rainfall data in the study.
The distribution of rain gauges is shown in Fig. 1.
These data have been widely used for different studies
previously and the qualities have been approved to be
reliable (Hu et al. 2007; Guo et al. 2008; Li et al.
2012). Daily precipitation data from all the stations are
Fig. 1 Location of Poyang
Lake basin and the distribution
of rain gauges (black squares
represent the six selected 0.25×
0.25° grids for statistical
comparison)
The high-resolution SPPs investigated in this study are
TRMM 3B42 and PERSIANN. TRMM was launched in
November 1997 as a joint effort by NASA and the
Japan Aerospace Exploratory Agency with the specific
objectives of studying and monitoring the tropical rainfall (Kummerow et al. 1998). The TRMM includes a
number of precipitation-related instruments, such as a
precipitation radar, a visible and IR sensor, and a special sensor microwave imager (SSM/I) like the TRMM
718
X. Li et al.
microwave imager (TMI) (Kummerow et al. 2001), and
detailed information is shown in Table 2. Several algorithms have been developed to make use of data from
the TRMM mission, and the purpose of the 3B42 class
of algorithm is to produce TRMM-adjusted merged IR
precipitation and root mean square precipitation error
estimates.
The TRMM 3B42 precipitation product was produced
using the following four steps (Vila et al. 2009). The first
stage of the algorithm consists of the calibration and combination of microwave precipitation estimates. Passive microwave observations from the TMI, Advanced Microwave
Scanning Radiometer for the Earth Observing System
(AMSR-E), and SSM/I are converted to precipitation estimates at the TRMM Science Data and Information System
with sensor-specific versions of the Goddard profiling algorithm (Kummerow et al. 2001). In the second step, the IR
precipitation estimates are created using the calibrated microwave precipitation. Histograms of time–space matched
combined microwave (high-quality precipitation rates) and
IR brightness temperatures (TBs), each represented on the
same three hourly 0.25×0.25° grid, are accumulated for
1 month into histograms on a 1×1° grid and aggregated to
overlapping 3×3° windows, which are then used to create
spatially varying calibration coefficients that convert IR TBs
to precipitation rates (Huffman et al. 2007; Vila et al. 2009).
In the third stage, the microwave and IR estimates are
combined. The physically based combined microwave estimates are taken “as is” where available, and the remaining
grid boxes are filled with microwave-calibrated IR estimates. And the final step is the indirect use of rain gauge
data. The GPCP monthly rain gauge analysis data developed
by the Global Precipitation Climatology Center and the
Climate Assessment and Monitoring System monthly rain
gauge analysis data developed by the CPC are integrated
using a histogram-matching technique (Huffman et al.
2007). A detailed description of this algorithm can be found
in Huffman et al. (2007) and Dinku and Anagnostou (2006).
The PERSIANN dataset, from University of California, Irvine, uses an adaptive neural network function
classification/approximation procedure to estimate rainfall rates at each 0.25×0.25° pixel of the IR TB image
provided by high-frequency (48 readings a day) geostationary satellites (Geostationary Operational Environmental Satellites (GOES)-8, GOES-9 and GOES-10;
Geostationary Meteorological Satellite-5, Meteorological
Satellite (MetSat)-6 and MetSat-7) (Hsu et al. 1997;
Sorooshian et al. 2000; Asadullah et al. 2008). Model
parameters are regularly updated using rainfall estimates
from low-orbit satellites, including the TRMM, the National Oceanic and Atmospheric Administration (NOAA)-15,
NOAA-16, and NOAA-17 satellites, and the Defense Meteorological Satellite Program (DMSP) F-13, DMSP F-14, and
DMSP F-15 satellites (Ferraro and Marks 1995; Kummerow
et al. 1998; Hsu and Sorooshian 2008). An adaptive training
feature facilitates updating of the network parameters whenever independent estimates of rainfall are available. Initially,
the neural network was trained using radar data and the input
was limited to TIR data and later extended to include the use
of both daytime visible imagery (Hsu et al. 1999) and the TMI
rainfall estimates (Sorooshian et al. 2000).
In the operation of PERSIANN, two PERSIANN algorithms are running in parallel (Hsu and Sorooshian 2008):
one is run in the simulation mode and the other in the update
mode. The simulation mode generates the surface rain rate at
the 0.25×0.25° resolution at every 30 min from the GEO
satellites IR images, while the update mode continuously
adjusts the mapping function parameters of PERSIANN
based on the fitting error of any pixel for which a PMW
instantaneous rainfall estimate is available. The simulation
mode generates the regular rainfall rate output, and the
update mode improves the quality of the product. A full
description of the algorithm was given by Sorooshian et al.
(2000) and Hsu et al. (1997).
TRMM 3B42 and PERSIANN precipitation estimates
are available at the 0.25×0.25° grid, three hourly and
six hourly resolution, respectively, with global coverage
between 50° N and 50° S, and the data used in the
study cover the period from January and March 2000,
respectively, to December 2007.
Table 2 Summary of the high-resolution SPPs
Datasets
Spatial
coverage
Temporal
coverage
Spatial
resolution
Temporal Main product data sources
resolution
TRMM 3B42 V6 50° S–50° N January 1998– 0.25×0.25° 3 hourly
globally
present
PERSIANN
50° S–50° N March 2000– 0.25×0.25° 6 hourly
globally
present
References
Geostationary IR, TRMM TMI, SSM/I, Huffman et al. (2007)
AMSU, AMSR-E, and rain gauge data
Neural network using geostationary IR, Hsu et al. (1997, 1999);
TRMM TMI, SSM/I, and AMSU
Sorooshian et al. (2000)
IR infrared, SSM/I special sensor microwave/imager, AMSU advanced microwave sounding unit, AMSR-E Advanced Microwave Sounding
Radiometer for the Earth Observing System
Assessing the performance of satellite-based precipitation products
Table 3 Contingency table for comparing SPPs with rain gauge data
3 Methods
To quantitatively compare SPPs with rain gauge observations,
several widely used validation statistical indices are selected
in the study. The correlation coefficient (R) is used to reflect
the degree of linear correlation between satellite-based precipitation and gauge observations, the mean error (ME) simply
scales the average difference between the satellite-based estimates and observations, the root mean square error (RMSE)
measures the average error magnitude but gives greater weight
to the larger errors, and the relative bias (BIAS) is used to
assess the systematic bias of satellite precipitation. The values
of R, ME, RMSE, and BIAS are calculated, respectively, as
Eqs. 1, 2, 3 and 4:
n P
Gi G Si S
i¼1
R ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
n 2
2
P
P
Gi G Si S
i¼1
i¼1
ð1Þ
ME ¼
719
n
1X
ð Si G i Þ
n i¼1
ð2Þ
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
1X
ð Si G i Þ 2
RMSE ¼
n i¼1
ð3Þ
Satellite-based rainfall
data
Greater than or equal to
the threshold
Less than the threshold
BIAS ¼
i¼1
ð Si G i Þ
n
P
100 %
ð4Þ
Less than the
threshold
a
b
c
d
The FAR measures the fraction of rain detections that was
actually false alarms. It ranges from 0 to 1, with a perfect score
of 0. The ETS provides the fraction of rain events (observed
and/or detected) which was correctly detected, and the perfect
score is 1 (Su et al. 2008; Koo et al. 2009). The ETS is
commonly used as an overall skill measure by the numerical
weather prediction community, whereas the FBI, POD, and
FAR provide complementary information about bias, misses,
and false alarms (Koo et al. 2009). Those indices have been
successfully applied in many studies (Layberry et al. 2006;
Ebert et al. 2007; Su et al. 2008; Kubota et al. 2009; Yong et
al. 2010; Shrestha et al. 2011) and are believed to be robust
and provide a sound basis for the assessment of the rainfall
detection capabilities of the satellite products. For a more
detailed explanation of FBI, POD, FAR, and ETS, please refer
to Wilks (2006) and Ebert et al. (2007) and their values are
calculated, respectively, using Eqs. 5, 6, 7, 8, and 9:
aþb
aþc
ð5Þ
POD ¼
a
aþc
ð6Þ
FAR ¼
b
aþb
ð7Þ
ETS ¼
a He
a þ b þ c He
ð8Þ
Gi
i¼1
where n is number of samples, Gi are gauge observations, Si
are satellite-based precipitation, and G and S are mean gauge
and satellite-based precipitation, respectively.
In addition, evaluation and comparison are carried out by
detecting rain events at different precipitation thresholds over
the Poyang Lake basin at a daily time step. It is performed by
computing the frequency bias index (FBI), probability of
detection (POD), false alarm ratio (FAR), and equitable threat
score (ETS) (Wilks 1995, 2006) based on a 2×2 contingency
table, as shown in Table 3. The FBI indicates whether the
dataset tends to underestimate (FBI<1) or overestimate (FBI>1)
rain events, and it ranges from 0 to infinity, with a perfect score
of 1. The POD gives the fraction of rain occurrences that was
correctly detected. It ranges from 0 to 1, with a perfect score of 1.
Greater than or equal to
the threshold
a the number of observed rain events correctly detected, b the number
of false alarms (rainfall events detected but not observed), c the number
of observed rain events not detected, d the sum of cases when neither
observed nor detected rain events occurred
FBI ¼
n
P
Rain gauges data
He ¼
ð a þ bÞ ð a þ c Þ
N
ð9Þ
where N is the total number of estimates, a is the number of
observed rain events correctly detected, b is the number of
false alarms (rainfall events detected but not observed), c
stands for the number of observed rain events not detected,
and d is the sum of cases when neither observed nor
detected rain events occurred.
1,527
1,682
1,573
1,796
1,771
1,594
1,657
1,585
1,990
1,430
1,640
1,718
1,349
1,619
152.37
117.8
149.2
152.4
163.3
189.2
154.0
265.5
187.8
222.8
333.5
286.5
511.2
301.2
361.1
258.2
233.5
290.1
280.4
274.2
282.9
62.3
46.6
54.1
72.7
70.1
80.5
64.4
149.9
120.5
106.0
147.4
134.6
304.1
160.4
222.0
152.4
149.7
136.7
156.4
129.5
157.8
8.8
8.2
8.8
9.4
9.2
8.2
8.8
18.3
17.2
15.8
17.2
18.7
17.1
17.4
14.5
13.9
13.4
14.7
14.5
13.8
14.1
Average rainfall in rainy day
b
a
Days with rainfall ≥1 mm
95
89
88
96
97
99
94
83
97
99
104
94
93
95
TRMM
Gauge
PERSIANN
TRMM
Gauge
PERSIANN
TRMM
Gauge
PERSIANN
TRMM
Gauge
Gauge
Max. daily rainfall (mm/day)
Average rainfallb (mm/day)
Rainy daya (day/year)
108
141
105
110
117
97
113
Gaoan
Jinggang
Ganzhou
Nancheng
Yiyang
Duchang
Average
Figure 2 shows the intensity distributions of daily rainfall in
different classes and their contributions to the total rainfall
Grids
4.2 Evaluation of the rainfall data under different rainfall
intensity
Table 4 Comparison of statistical indexes between satellite-based and rain gauge rainfall
For the comparison between satellite-based rainfall and rain
gauge data at grid scale, several statistical indices such as
average rainy day in a year, average rainfall in rainy days,
maximal daily rainfall, maximal 5-day rainfall, and average
annual rainfall were firstly analyzed. Considering the relative
location of rain gauges in the grid (central is best), representative of Poyang Lake basin’s topography and the spatial
distribution in the catchment, the six grids were selected for
the comparison between satellite pixel (0.25×0.25° grid) and
the gauging stations inside the grids (namely, Gaoan,
Jinggang, Ganzhou, Nancheng, Yiyang, and Duchang) (see
Fig. 1) and the results were shown in Table 4. It is seen that the
average rainy days (rainfall ≥ 1 mm/day) were 97–
141 days/year for different rain gauges, but 83–104 and 88–
99 days/year for TRMM 3B42 and PERSIANN, respectively.
This indicated that average rainy days were underestimated by
both SPPs. The average rainfall (in rainy days) is another
important and useful index to reflect the precision of rainfall
amount. The average rainfalls estimated from rain gauge data
ranged between 13.4 and 14.7 mm/day, with an average of
14.1 mm/day. However, the average rainfalls from TRMM
3B42 data were larger than those from rain gauge data in every
grid, and the opposite was true for PERSIANN. As for the
maximal daily rainfall, the TRMM 3B42 data were smaller
than rain gauge data, except in Nancheng and Duchang grid,
while PERSIANN data were acutely smaller than rain gauge
data in every grid. The similar situations were observed further in the comparison of maximal 5-day rainfall. It is shown
that the maximal 5-day rainfall from PERSIANN data were
lower markedly than that from rain gauge data, but the performance of TRMM 3B42 was acceptable. Lastly, the average
annual total rainfall estimated from rain gauge, TRMM 3B42,
and PERSIANN data were 1,619, 1,657, and 839 mm, respectively. TRMM 3B42 overestimated the annual rainfall slightly,
but PERSIANN underestimated it greatly.
Max. 5-day rainfall (mm/5day)
4.1 Grid-based statistical comparison
TRMM
4 Results
PERSIANN
Annual rainfall (mm/year)
All of these indicators are calculated based on the domainaveraged precipitation amount over Poyang Lake basin.
Moreover, to quantify the ability of each dataset in predicting
light and heavy rainfall events, the FBI, POD, FAR, and ETS
are calculated for precipitation thresholds of 1, 2, 5, 10, 25,
and 50 mm/day, respectively.
853
744
787
913
908
827
839
X. Li et al.
PERSIANN
720
Assessing the performance of satellite-based precipitation products
in different grids. It is seen that nonrainy and puny rain
(<1 mm/day) had the largest occurrence, occurring about
70 % of the total days, in all datasets. Difference between
satellite-based data and rain gauge data was also quite small,
except in Jinggang grid that was located in a mountainous
area which may bring uncertainties to the observation. The
occurrence of the small rainfall class ranges (1 mm/day<
rainfall ≤3 mm/day) from TRMM 3B42 was lower than
those from rain gauge data, but it was larger for
PERSIANN. It can also be seen that, although the occurrences of the first two classes, i.e., nonrainy/puny rain and
small rain classes, accounted for as high as 70–80 % of the
total days, their contributions to the total rainfall amount
were very small. The occurrence of the middle rainfall class
721
ranges (3 mm/day < rainfall ≤ 25 mm/day) estimated by
TRMM 3B42 and PERSIANN were generally equivalent
(accounting for about 17 % on average) to that of rain gauge
data, but with different contribution rates to the total rainfall.
For the TRMM 3B42, rainfall contribution rates were mildly
lower than that of rain gauge rainfall in classes of 3–10 and
10–25 mm/day. But for the PERSIANN, the contribution
rates were overestimated in both classes.
It is important to note that the high rainfall ranges
(>25 mm/day) play a significant role in contributing to the
total rain amount. This kind of information is essential because thunder showers cause the geographical slides and flash
floods and hence threaten the economy and human life. Although the two high rainfall classes (25–50 and >50 mm/day)
Fig. 2 Distribution of daily rainfall in different rainfall classes and their relative contributions to the total rainfall in different grids
722
occurred only about 5 % of the total days together, their
contributions to the total rainfall were as high as 30 and
22 %, respectively, for rain gauge data. TRMM 3B42
performed perfectly for both occurrence and contribution rates
in the rainfall class of 25–50 mm/day and the statistics
matched well with their counterparts in every grid. However,
in the rainstorm class (>50 mm/day), rainfall contribution
rates were larger than that of observation data. PERSIANN
obviously underestimated both occurrence and contribution
rates for the high rainfall ranges, especially for the class of
>50 mm/day. So, the SPPs had difficulties in accurately estimating the rainstorm in Poyang Lake basin, TRMM 3B42
inclined to overestimate the occurrence and contribution rates,
whereas PERSIANN usually underestimated them.
In order to further elucidate the differences between the two
datasets, a rain event detection analysis over the Poyang Lake
basin had also been performed. Figure 3 shows the verification results of FBI, POD, FAR, and ETS scores for domainaveraged daily precipitation at different rainfall thresholds. It
is found that the TRMM 3B42 tended to overestimate the
frequency of intense rain events slightly, but there was a
systematic underestimation of precipitation occurrence by
PERSIANN, as shown in Fig. 3a. The FBI values of the latter
decreased from 0.7 to 0.1 as the precipitation threshold increases, indicating that the PERSIANN products were less
skillful to correctly capture the magnitude of intense rain
events. Figure 3b shows that the POD of both SPPs had a
consistent trend and decreased as the precipitation threshold
increases, and the POD values of TRMM 3B42 tended to be
higher than those of the PERSIANN products. The TRMM
3B42 produced a fine result, with POD values being larger
than 0.70 for thresholds 1–5 mm/day, while these values
decreased rapidly (POD < 0.5) for thresholds of 25 and
Fig. 3 Precipitation detection
of daily average TRMM 3B42
and PERSIANN data versus
rain gauges at different rainfall
thresholds (a FBI, b POD, c
FAR, d ETS)
X. Li et al.
50 mm/day. Oppositely, the FAR results (Fig. 3c) show an
increasing trend as the precipitation threshold increases.
TRMM 3B42 and PERSIANN had an equivalent performance
with the small FAR scores up to the threshold of 10 mm/day,
but the FAR of the latter increased rapidly (>0.7) for precipitation threshold >25 mm/day. The ability to detect rain events
was also evaluated in terms of the ETS. Both SPPs showed
increasing ETS scores for the precipitation thresholds up to
2 mm/day; then, the ETS scores started dropping for the higher
thresholds (Fig. 3d). On the other hand, the ETS scores of
TRMM 3B42 were higher than PERSIANN in all precipitation
thresholds. Figure 3d, together with Fig. 3b, c, demonstrated
that SPPs (TRMM 3B42 and PERSIANN) can identify the
small rain events but failed to capture the intense rain events,
especially for PERSIANN precipitation products.
4.3 Temporal characteristics of SPPs performance
The distribution of monthly rainfall for various precipitation
estimates was summarized using box plot for the mean,
upper and lower quartiles, and max and min of rainfall as
shown in Fig. 4. From the rain gauge data, we can see that
the rainfall of Poyang Lake basin increased very fast from
January and reached its peak from April to June, then the
rainfall decreased sharply from July to September and the
dry season set in and lasted through December. Both
TRMM 3B42 and PERSIANN data described this distribution characteristic correctly, but the latter tended to suffer
from a systematic underestimation of monthly rainfall, regardless of maximum, minimum, or mean.
In Fig. 5 several statistical indices, such as RMSE, ME, and
BIAS, of monthly averaged daily rainfall between satellitebased and rain gauge data are shown. It is noticeable from
Assessing the performance of satellite-based precipitation products
723
Fig. 4 Box plot of monthly
rainfall for different
precipitation products
Fig. 5a that RMSEs ranged from 4 to 10 mm and showed
similar temporal pattern for both SPPs. Greater RMSEs were
mainly observed in the wet season (April to June), but the lower
values were mainly observed in the dry season (October to
December). However, the MEs of two SPPs presented the
opposite structure (as Fig. 5b). TRMM 3B42 showed positive
errors in general and ME values were <1 mm, while the
negative errors were mainly found in PERSIANN with large
ME values even more than −3 mm. Like ME, BIAS of both
SPPs also showed a similar temporal pattern. PERSIANN had
a better performance in the summer season with the smaller
BIAS (almost 0 in July), but it performed much worse in the
winter season with the BIAS value as much as −80 % (Fig. 5b).
4.4 Spatial characteristics of SPPs performance
A spatial performance analysis was adopted to examine and
compare the spatial variability of SPPs. Figure 6a–c shows
the spatial distribution of averaged annual rainfall for the
2000–2007 period derived from rain gauges, TRMM 3B42,
and PERSIANN, respectively. The spatial distribution of
rain gauge data was directly interpolated by the IDW technique with a power of 2. The rainfall characteristics varied
strongly in different areas (Fig. 6a). The largest annual
rainfall occurred in the eastern part of Poyang Lake basin
(with annual rainfall as high as 2,000 mm), while the lowest
one was observed in the southern and northern parts (about
1,400 mm). The median rainfall (about 1,600–1,800 mm)
was observed in other areas, i.e., the central parts of the
basin. Overall, good agreement existed between the SPPs
and rain gauge estimates in terms of relative values within the
basin. Both TRMM 3B42 and PERSIANN showed higher
Fig. 5 Annual distribution of a
RMSE and b ME and BIAS
between satellite-based and rain
gauge data
rainfall rates in the eastern side of the basin than at the central
and western sides, although the high rainfall rates covered a
larger area than that of the rain gauge estimates (Fig. 6b, c).
However, absolute values varied considerably from one
dataset to another. For instance, the northeast–northwest rainfall gradient observed in rain gauge estimates, as shown in
Fig. 6a, was not reproduced in satellite-based estimates, and
local median rainfall in the central parts of the basin was
weakened in TRMM 3B42 and PERSIANN. Visual inspections of the results also revealed that the lowest rainfall
amount (about 1,050 mm for TRMM 3B42 and 770 mm for
PERSIANN) and their distribution regions differ from that of
rain gauge estimates, especially for PERSIANN. These biases
maybe caused by two aspects: on one hand, there was a
weakness for both SPPs to accurately reflect the spatial distribution of precipitation in some regions; on the other hand, a
great deal of uncertainties was also exhibited in the spatial
distribution of rain gauge data due to the sparsity and bad
quality of rain gauges in mountain area as well as the weakness of the interpolation technique.
Subsequently, the spatial distribution of R, ME, RMSE,
and BIAS between satellite-based and rain gauge data was
also examined and compared, which was calculated for
every rain gauge and their nearest satellite pixel (0.25×
0.25° grid) during the period of 2000–2007 at the daily
scale. The results are shown in Fig. 7. The TRMM 3B42
correlated best with the rain gauge observations, with
most R values >0.45 (even >0.55). While for PERSIANN,
R values mainly ranged between 0.35 and 0.45. The ME
values varied considerably in the two SPPs. TRMM 3B42
showed smaller MEs in the central parts of the basin, with
the ME falling into the −0.5 to 0.5 mm class; only several
724
X. Li et al.
Fig. 6 Spatial distribution of average annual rainfall for the 2000–2007 period derived from a rain gauges, b TRMM 3B42, and c PERSIANN
pixels in the peripheral area produced small positive errors
(0.5–1.5 mm). However, the PERSIANN products
presented large negative errors in all calculated pixels.
Evaluated from the index of the RMSE, PERSIANN
performed better than TRMM 3B42. The RMSE values
of the former (about 11 mm) were lower than the latter
(about 12–14 mm). As for BIAS, its spatial distribution
was similar to ME, TRMM 3B42 performed better with
smaller relative bias than PERSIANN in general.
4.5 Dependence of SPPs performance on elevation and slope
This study also investigated the dependence of SPPs performance on elevation and slope. In order to do so, statistical
Fig. 7 Spatial distribution of R, ME, RMSE, and BIAS between satellite-based and rain gauge data
Assessing the performance of satellite-based precipitation products
725
Fig. 8 Scatter plots of the correlation coefficient and RMSE versus elevation over the Poyang Lake basin
indices such as the correlation coefficient and RMSE between satellite-based and rain gauge data were used, and for
elevation, a logarithm transformation (ln(Elevation), simply
denoted by ln(E)) was made to obtain a better fit at the
lowest values. Figure 8 shows the scatter plots of the correlation coefficient and RMSE versus ln(E) over the Poyang
Lake basin. It is seen that the correlation coefficient as well
as RMSE has a clear dependence on elevation in both SPPs,
and their relationship can be fitted using quadratic equations. The correlation coefficient, either from TRMM 3B42
or PERSIANN, reached maximum at approximately ln(E)=
4.5, and then started dropping for higher ln(E). The RSME
values for TRMM 3B42 decreased with increasing ln(E) at
lower elevation (ln(E)<4.5) and increased after that. The
RMSE from PERSIANN had a feeble dependence on ln(E)
at lower elevation and increased clearly at higher elevation.
The scatter plots of the correlation coefficient and RMSE
versus slope are shown in Fig. 9. It is found that the correlation coefficient and RMSE varied with the slope in both
SPPs, and their relationships can also be fitted using quadratic equations. Similar with Fig. 8, the correlation coefficient reached a maximum at slope of about 0.2 and then
dropped when the slope became steeper for both SPPs. The
RMSE of both SPPs presented slight decreasing trends at
gently sloping area but became larger at steeper area.
In general, TRMM 3B42 performed best within the ln(E)
range of 4.0–5.0 and slope range of 0.1–0.3, but for
PERSIANN, the dependence on elevation and slope were
trivial at lower elevation and gently sloping area. At higher
elevation (ln(E)>5.0) and steeper area (slope>0.3), the validity of both SPPs decreased with increasing elevation and
slope. Similar conclusions were also indicated by Barros et
al. (2006), which found that the TRMM’s precipitation radar
has difficulties in detecting precipitation at high elevations.
5 Discussions
The previous section presented the error statistics, which
showed that the different satellite rainfall products have very
different strengths and weaknesses under different rainfall
intensities and in different seasons. Possible sources of
errors may be associated with the effects of different sensors, topographies, and retrieval algorithms used in the
rainfall estimates (Beighley et al. 2011).
Poyang Lake basin consists of mountainous and hilly
areas, where the complex topography could cause strong
scattering signals in the microwave region, especially at
cold land surfaces and ice-covered or snow-covered
areas (Huffman et al. 2007; Scheel et al. 2011) and
also a strong influence on TB and its polarization property with varying snow cover conditions, depending on
exposure and the altitude in mountainous terrain
(Amlien 2008; Scheel et al. 2011). Mountainous regions
have relatively warm clouds, and the satellite sensors
may not detect the rainfall from the warm clouds as the
cloud tops would be too warm for IR thresholds to
discriminate between raining and nonraining clouds
Fig. 9 Scatter plots of the correlation coefficient and RMSE versus slope over the Poyang Lake basin
726
(Hong et al. 2007; Dinku et al. 2008; Bitew and
Gebremichael 2010). Moreover, clouds over mountainous area could produce heavy rainfall without much ice
aloft in PMW algorithms (Dinku et al. 2010). However,
the sensors could accurately detect rainfall from the
deep convection, as Fig. 5b shows a better performance
in the summer season with the smaller BIAS (almost
0 in July) for PERSIANN data. Zhou et al. (2008) also
gained similar conclusions that rainfall is more convective with higher rainfall intensities during the warm
season and could be accurately estimated in satellite
precipitation products. But, on the other hand, the heavy
rainfall may cause signal attenuation which is significant
and most frequently encountered (Villarini and
Krajewski 2010). This is a possible explanation for the
bad performances under higher rainfall intensity for both
SPPs (Fig. 3).
Additionally, although the topography obviously influences the accuracy of satellite products, the retrieval
algorithm may significantly dominate the contributions
of satellite error sources for high-resolution estimates
(Yan and Gebremichael 2009; AghaKouchak et al.
2009). The current global algorithms estimate precipitation indirectly from the TB at the cloud top (Levizzani
and Amorati 2002) and do not consider the altitude of
the object and the sub-cloud evaporation (Scheel et al.
2011; Dinku et al. 2011), which significantly affect the
retrieval accuracy of precipitation (Petty 2001). At the
same time, further challenges arise from the processing
scheme for microwave and IR data (Scheel et al. 2011).
The definition of the underlying surface should satisfy
the interpretation of the measured microwave signal and
globally applied algorithms need to cope with highly
heterogeneous terrain with varying TBs (Scheel et al.
2011). Furthermore, it is indispensable to calibrate the
retrieval algorithms using locally available rain gauge
observations, which is not just for the selection of
appropriate temperature thresholds but also involves determining the other relevant calibration parameters
(Dinku et al. 2008, 2011). Local calibration could be
one of the most potent approaches to alleviate the
satellite precipitation errors.
Certainly, the current research is only limited to evaluate
and compare the SPPs at the daily scale. The sub-daily (or
three hourly) precipitation data are not involved due to the
temporal scale limitation of rain gauge data, although they
are more critical to drive the flood warning models and
decision-making systems in Poyang Lake basin. Moreover,
the effects of topography are more complex, as it includes
other factors than just elevation and slope, i.e., the orientation of the slope with respect to wind direction at a given
time, and geographical location of the slopes (Dinku et al.
2008). The current research is limited to describing the roles
X. Li et al.
that elevation and slope might have played in the performance of satellite rainfall products; however, the physical
mechanisms are not addressed adequately here. So, extensive efforts on the evaluation of satellite-based products and
thorough understanding of the errors in satellite rainfall need
to continue in Poyang Lake basin as well as in other regions.
6 Conclusions
This paper evaluated and compared two widely used highresolution satellite-based precipitation data (TRMM 3B42
V6 and PERSIANN) with rain gauge data in Poyang Lake
basin and investigated their spatial and temporal characteristics, including their relationship with evaluation and slope.
It is concluded that:
&
&
&
&
&
TRMM 3B42 and PERSIANN were better suited to
determining rain occurrence frequency than to determining the rainfall amount. In the study region, the former
slightly underestimated the rainfall amount contribution
rates in middle rainfall class ranges (3 mm/day<rainfall≤25 mm/day) but overestimated it in the heavy rainfall class (> 50 mm/day), and the opposite trend was
observed for PERSIANN.
The temporal distribution characteristics of monthly
rainfall were correctly described by both SPPs, and
greater RMSEs were mainly observed between April
and June. PERSIANN performed much worse in the
winter season with the BIAS value being as much
as −80 %, while TRMM 3B42 gained better accuracy
in the winter season than in the summer season.
TRMM 3B42 had better performance in estimating the
frequency and locality of precipitation occurrence and
had potential for useful application in regions where rain
gauge observations were sparse or of bad quality. Shortcomings of TRMM 3B42 are as follows: it usually underestimates the rainy days and overestimates the
average rainfall and intense rain events, which may
reduce the accuracy of land surface hydrological processes simulation or flood forecasting.
PERSIANN tended to suffer from a large systematic
underestimation of rainfall, and during high precipitation events, the occurrence frequency and rainfall
amount were underestimated greatly, which clearly revealed that IR-based rainfall algorithms had major limitations in reproducing rainfall fields in the Poyang Lake
basin.
The performances of both SPPs had clear dependence on
elevation and slope and their relationships can be fitted
using quadratic equations. TRMM 3B42 and PERSIANN
performed best at a gently rolling landscape and the accuracy would decrease at higher elevation or steeper area.
Assessing the performance of satellite-based precipitation products
These conclusions indicate that efforts are necessary
to further improve the current algorithms to reduce false
alarms and missed precipitation and capture the heavy
rain events correctly. Especially for PERSIANN, it is
indispensable to incorporate additional information, such
as relative humidity (Janowiak et al. 2001, 2004) and/or
rain gauge data (Thorne et al. 2001), into the IR-based
algorithms to improve the accuracy of rainfall estimates.
On the other hand, it is an exigent need to develop and
improve the adjustment procedure of hydrological
models and flood warning models to advance the utilization of satellite-based precipitation data in practical
applications (i.e., flood forecasting and warning).
Acknowledgments This work is jointly funded by the National
Basic Research Program of China (973 Program) (2012CB417003
and 2012CB956103-5), the National Natural Science Foundation
of China (41101024), and the Science Foundation of Nanjing
Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS2012135001 and NIGLAS2010XK02). The authors are grateful to the anonymous reviewers and the editor
who helped in improving the quality of the original manuscript
and Dr. Qing Zhu from Nanjing Institute of Geography and
Limnology, CAS for providing valuable improvements to the
earlier manuscript. Thanks also to Dr. Jian Liu and Dr. Yuanbo
Liu from Nanjing Institute of Geography and Limnology, CAS for
providing daily rain gauge data in Poyang Lake basin.
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